The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

By B. Thyagarajan, Jesse D. Bloom. 2014

203 citations recorded by the source corpus.

Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the ≈ 104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the protein’s structure and function, and can be used to create a model that describes hemagglutinin’s evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenza’s antigenic evolution.

Subfields: Epidemiology, Molecular Biology

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Citations in the corpus

Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin (2016, methodology): “Figure 2A shows normalized synonymous mutation frequencies across HA for each of the three replicate mutant virus libraries from both our previous deep mutational scan of HA that utilized reverse genetics [4], and the current study utilizing helper viruses.”
A perspective on the structural and functional constraints for immune evasion: insights from the influenza virus (2017, background): “While a large portion of HA1 amino-acid sequence is highly variable [22] and is intrinsically tolerable to mutations, the receptor-binding site (RBS) is an exception [29, 30].”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, background): “…many or long trajectories of substitutions using the full ﬁtness matrix may still be troublesome, although this has been done experimentally for a small number of proteins with very short trajectories (McLaughlin et al. 2012; Gong et al. 2013; Roscoe et al. 2013; Thyagarajan and Bloom 2014).”
Predicting evolution (2017, background): “Site-specific amino acid preferences can be inferred from deep sequencing data 104 using equilibrium models of molecular evolution 105,106 ; related methods map epistatic interactions between these sites 16,107 .”
Adaptation in protein fitness landscapes is facilitated by indirect paths (2016, background): “Recently, the bottleneck in experimental throughput has been improved substantially by coupling saturation mutagenesis with deep sequencing (Fowler et al., 2010; Hietpas et al., 2011; Jacquier et al., 2013; Wu et al., 2014; Thyagarajan and Bloom, 2014; Qi et al., 2014; Stiffler et al., 2015) , which”
Multi-state design of flexible proteins predicts sequences optimal for conformational change (2019, background): “Compared to experimental approaches that have tested for functionally tolerated mutations in either dengue virus envelope protein or influenza hemagglutinin protein [2, 43, 44], RECON MSD is much faster and less costly in identifying biologically relevant mutations.”
A human coronavirus evolves antigenically to escape antibody immunity (2020, background): “Specifically, mutations to influenza hemagglutinin are often well tolerated [68], and single hemagglutinin mutations can have huge effects on escaping polyclonal sera [69].”